Brian (Po-Yen) Tung

Inverse design of superconducting materials requires generative models that can optimize functional properties without collapsing to narrow regions of known chemistry. We study this problem using diffusion-based generative models fine-tuned via reinforcement learning (RL) with surrogate feedback, where a graph neural network predictor of superconducting critical temperature (T_c) provides the reward signal. Compared with classifier-free guidance (CFG), RL increases the average surrogate-predicted T_c from 10 K to 17 K. To address diversity collapse during feedback-based optimization, we further merge independently fine-tuned models, which increases the fraction of unique chemical systems by over 7% relative to single-model fine-tuning. We quantify exploration using a calibrated out-of-distribution metric, OOD@α, and find that the merged RL model generates >92% of samples beyond the 95th percentile of the calibration distribution (OOD@0.05) vs. 21% for CFG-based baselines. This increased exploration does not degrade structural validity, with S.U.N. (stable, unique, and novel) rates improving from 0.19 under CFG to 0.57 under RL. We further validate 198 selected RL-generated candidates using first-principles electron-phonon Wannier (EPW) calculations. Although surrogate and EPW T_c predictions show weak pointwise agreement on this OOD set, the EPW-computed T_c distribution is shifted upward relative to the training data (13.87 \pm 4.56 K), showing that RL increases the yield of OOD candidates enriched for higher independently computed EPW T_c. Overall, RL combined with model merging provides a practical framework for generating physically plausible, diverse, and OOD superconductor candidates, while first-principles validation remains necessary for final quantitative ranking.

Inferring optimal solutions from limited data is considered the ultimate goal in scientific discovery. Artificial intelligence offers a promising avenue to greatly accelerate this process. Existing methods often depend on large datasets, strong assumptions about objective functions, and classic machine learning techniques, restricting their effectiveness to low-dimensional or data-rich problems. We present a deep active learning pipeline that combines deep neural networks with a novel tree search to find superior solutions in high-dimensional complex problems with non-cumulative objectives and limited data availability. Our pipeline iteratively approaches the optimum using a neural surrogate and introduces new search mechanisms to bypass the local optimum and minimize the number of samples needed to achieve superior solutions. These contributions enable our pipeline to achieve superior solutions across diverse problems with up to 2,000 dimensions, whereas existing methods are limited to 100 dimensions and require up to 10 times more data points. Our pipeline demonstrates wide applicability, discovering superior solutions in various domain science problems. This advancement enables data-efficient knowledge discovery and opens the path towards scalable self-driving laboratories. Although we focus on problems within the realm of scientific domain, the advancements achieved herein are applicable to a broader spectrum of challenges across all quantitative disciplines.

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10−6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.